19 citations · 34 across the 10 of their papers we have counts for
12 papers
UBTree: Parallel Tree Drafting via Unigram and Bigram Models for Speculative Decoding
Chumeng Liang, Linxuan Wang, Xinyu Peng +6
Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters have achieved breakthrough perf…
LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling
Yuxin Chen, Chumeng Liang, Hangke Sui +4
Continuous diffusion has been the foundation of high-fidelity, controllable, and few-step generation of many data modalities such as images. However, in language modeling, prior co…
Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein Regularization
Jiajun Fan, Shuaike Shen, Chaoran Cheng +3
Recent advancements in reinforcement learning (RL) have achieved great success in fine-tuning diffusion-based generative models. However, fine-tuning continuous flow-based generati…
Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models
Zherui Huang, Yicheng Liu, Chumeng Liang +1
Deploying learned decision-making systems often requires transferring to new sites where the sensing pipeline differs. In such cases, observations can change in semantics and dimen…
Real-World Benchmarks Make Membership Inference Attacks Fail on Diffusion Models
Chumeng Liang, Jiaxuan You
Membership inference attacks (MIAs) on diffusion models have emerged as potential evidence of unauthorized data usage in training pre-trained diffusion models. These attacks aim to…
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion
Xiaoyu Wu, Yang Hua, Chumeng Liang +4
Diffusion Models (DMs) have evolved into advanced image generation tools, especially for few-shot generation where a pretrained model is fine-tuned on a small set of images to capt…
Toward effective protection against diffusion based mimicry through score distillation
Haotian Xue, Chumeng Liang, Xiaoyu Wu +1
While generative diffusion models excel in producing high-quality images, they can also be misused to mimic authorized images, posing a significant threat to AI systems. Efforts ha…
Targeted Attack Improves Protection against Unauthorized Diffusion Customization
Boyang Zheng, Chumeng Liang, Xiaoyu Wu
Diffusion models build a new milestone for image generation yet raising public concerns, for they can be fine-tuned on unauthorized images for customization. Protection based on ad…
FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph
Zhanyu Liu, Chumeng Liang, Guanjie Zheng +1
This paper proposes the fine-grained traffic prediction task (e.g. interval between data points is 1 minute), which is essential to traffic-related downstream applications. Under t…
Mist: Towards Improved Adversarial Examples for Diffusion Models
Chumeng Liang, Xiaoyu Wu
Diffusion Models (DMs) have empowered great success in artificial-intelligence-generated content, especially in artwork creation, yet raising new concerns in intellectual propertie…
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
Chumeng Liang, Xiaoyu Wu, Yang Hua +6
Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate no…
CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation
Chumeng Liang, Zherui Huang, Yicheng Liu +7
Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and sho…